Architecting, Developing, nixCraft, DevOps, AI/ML, Blockchain

Linear Algebra: Matrices 1

Linear Algebra Crash Course for Programmers Part 2a

This article on matrices is part two of an ongoing crash course on programming with linear algebra, demonstrating concepts and implementations in Python. The following examples will demonstrate some of the various mathematical notations and their corresponding implementations, easily translatable to any programming language with mature math libraries. This series began with Linear Algebra: Vectors Crash Course for Python Programmers Part 1. Vectors are the building blocks of matrices, and a good foundational understanding of vectors is essential to understanding matrices.

Linear Algebra: Vectors

Crash Course for Python Programmers Part 1

This article on vectors is part of an ongoing crash course on linear algebra programming, demonstrating concepts and implementations in Python. The following examples will demonstrate some of the algebraic and geometric interpretations of a vector using Python. A vector is an ordered list of numbers, represented in row or column form. This small series of articles on linear algebra is meant to help you prepare for learning the deeper concepts related to Machine Learning and math that drives the higher level abstractions provided by many of the libraries available today.

Kafka on Kubernetes

Deploy a highly available Kafka cluster on Kubernetes.

Kafka is a fast, horizontally scalable, fault-tolerant, message queue service. Kafka is used for building real-time data pipelines and streaming apps. There are a few Helm based installers out there including the official Kubernetes incubator/kafka. However, in this article, I walk through applying a surprisingly small set of Kubernetes configuration files needed to stand up high performance, highly available Kafka. Manually applying Kubernetes configurations gives you a step-by-step understanding of the system you are deploying and limitless opportunities to customize.

Ethereum Ethstats

Learning the Ethereum Blockchain through its metrics.

The eth-netstats project provides a great dashboard interface for monitoring the status of an Ethereum Blockchain from the perspective of its nodes. The website reports statistics from an extensive list of Ethereum nodes on the public Ethereum Blockchain, however, the eth-netstats software that drives can also be used to monitor a Private Ethereum Blockchchain as I demonstrate in the previous article Deploy a Private Ethereum Blockchain on a Custom Kubernetes Cluster.

Ethereum Blockchain on Kubernetes

Deploy a Private Ethereum Blockchain on a Custom Kubernetes Cluster.

Blockchain technologies have been made famous by Cryptocurrencies such as Bitcoin and Ethereum. However, the concepts behind Blockchain are far more reaching than their support for cryptocurrency. Blockchain technologies now support any digital asset, from signal data to complex messaging, to the execution of business logic through code. Blockchain technologies are rapidly forming a new decentralized internet of transactions. Kubernetes is an efficient and productive platform for the configuration, deployment, and management of private blockchains.


A Conceptual and Motivational Overview

The Internet of Transactions Blockchain may be a recent entry to the technology landscape. However, it has quickly become an essential iteration in the evolution of peer-to-peer communication and distributed computing. Originally developed as a way to protect digital currency, Blockchain technologies now support any digital asset, from signal data to complex messaging, to the execution of business logic through code. Blockchain technologies are rapidly forming a new decentralized internet of transactions.

Kubernetes Port Forwarding for Local Development

Using kubefwd

kubefwd helps to enable a seamless and efficient way to develop applications and services on a local workstation. Locally develop applications that intend to interact with other services in a Kubernetes cluster. kubefwd allows applications with connection strings like http://elasticsearch:9200/ or tcp://db:3306 to communicate into the remote cluster. kubefwd can be used to reduce or eliminate the need for local environment specific connection configurations. I don't run Kubernetes locally, but I do depend on services running in remote clusters.

FaaS on Kubernetes

Kubeless, Python and Elasticsearch

FaaS or Function as a Service also known as Serverless computing implementations are gaining popularity. Discussed often are the cost savings and each implementations relationship to the physical and network architecture of a specific platform or vendor. While many of the cost and infrastructure advantages of FaaS are compelling, its only one of many advantages. Below, I hope to demonstrate how easy it is to develop and deploy FaaS components into a custom Kubernetes cluster.

Elasticsearch Essential Queries

Getting started with Elasticsearch

The following is an overview for querying Elasticsearch. Over the years I have tried to assemble developer notes for myself and my team on a variety of platforms, languages and frameworks, a type of cheat-sheet but with context, not a comprehensive how-to, but a decent 15-minute overview of the features we are most likely to implement in a given iteration. Explore the Elasticsearch official documentation: Search in Depth. Contents Motivation Following Along with Elasticsearch and Kubernetes Vocabulary Basic CRUD API Delete an Index Create an Index Create or Update a Document (Upsert) Get a Document Mappings, Types and Metadata Get Mapping Create a Mapping Searching Range Filtering Aggregations Counts Averages, Minimums and Maximums Percentile Percent by Rank Percent by Rank Interval Resources (adsbygoogle = window.

Remote Query Elasticsearch on Kubernetes

Local workstation-based microservices development

Developing on our local workstations has always been a conceptual challenge for my team when it comes to remote data access. Local workstation-based development of services that intend to connect to a wide range of remote services that may have no options for external connections poses a challenge. Mirroring the entire development environment is possible in many cases, just not practical. In days before Kubernetes, writing code in IDEs on our local workstation meant we had only a few options for developing server-side-API-style services that needed to connect to a database.

High Traffic JSON Data into Elasticsearch on Kubernetes

Instant, reliable, send and forget.

IOT devices, Point-of-Sale systems, application events or any client that sends data destined for indexing in Elasticsearch often need to send and forget, however, unless that data is of low value there needs to be assurance that arrives at its final destination. Back-pressure and database outages can pose a considerable threat to data integrity. Contents Background Overview Development Environment the-project Namespace The Project: Weather (wx) Data rxtx for Store-and-Forward wx-rxtx Service wx-rxtx StatefulSet rtBeat to Collect, Buffer and Publish wx-rtbeat Service wx-rtbeat ConfigMap wx-rtbeat Deployment Client Simulation / Kubernetes Cron Performance Conclusion Port Forwarding / Local Development Reference (adsbygoogle = window.

Kibana on Kubernetes

Visualize your Elasticsearch data.

This guide walks through a process for setting up Kibana within a namespace on a Kubernetes cluster. If you followed along with Production Grade Elasticsearch on Kubernetes then aside from personal or corporate preferences, little modifications are necessary for the configurations below. Contents Project Namespace Service Kibana ConfigMap Deployment Basic Auth TLS Certificate (Optional) Ingress Conclusion Port Forwarding / Local Development Resources (adsbygoogle = window.

Production Grade Elasticsearch on Kubernetes

Setup a fast, custom production grade Elasticsearch cluster.

Installing production ready, Elasticsearch 6.2 on Kubernetes requires a hand full of simple configurations. The following guide is a high-level overview of an installation process using Elastic’s recommendations for best practices. The Github project kubernetes-elasticsearch-cluster is used for the Elastic Docker container and built to operate Elasticsearch with nodes dedicated as Master, Data, and Client/Ingest. The Docker container docker-elasticsearch, a “Ready to use, lean and highly configurable Elasticsearch container image.

Kubernetes Team Access - RBAC for developers and QA

Role Based Access Control

RBAC (Role Based Access Control) allows our Kubernetes clusters to provide the development team better visibility and access into the development, staging and production environments than it has have ever had in the past. Developers using the command line tool kubectl, can explore the network topology of running microservices, tail live server logs, proxy local ports directly to services or even execute shells into running pods. Kubernetes and GitlabCI are the central components of our DevOps toolchain and have increased our productivity by many multiples over the traditional approaches of the past.

Python Data Essentials - Matplotlib and Seaborn

A beginners guide.

There is an overwhelming number of options for developers needing to provide data visualization. The most popular library for data visualization in Python is Matplotlib, and built directly on top of Matplotlib is Seaborn. The Seaborn library is “tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.” This article is only intended to get you started with Matplotlib and Seaborn.

Webpage to PDF Microservice

Automate PDF Report Generation

I create a lot of data visualizations for clients, many of which are internal, portal-style websites that present data in real time, as well as give options for viewing reports from previous time-frames. PDFs are useful for data such as bank statements or any form of time-snapshot progress reporting. It is common for clients to want PDF versions generated on a regular basis for sharing through email or other technologies.

A Microservices Workflow with Golang and Gitlab CI

Continuous Integration & Deployment

Many of the resources on Cloud Native Microservices show you how easy it is to get up and running with AWS or GKE. I think this is great but for the fact that I see a trend (in my clients at least) of associating concepts with particular products or worse, companies. I love Amazon, but it’s not THE cloud). In my opinion, to embrace Cloud Native and Microservices you should develop some, and host them yourself.

Python Data Essentials - Pandas

A data type equivalent to super-charged spreadsheets.

Pandas bring Python a data type equivalent to super-charged spreadsheets. Pandas add two highly expressive data structures to Python, Series and DataFrame. Pandas Series and DataFrames provide a performant analysis and manipulation of “relational” or “labeled” data similar to relational database tables like MySQL or the rows and columns of Excel. Pandas are great for working with time series data as well as arbitrary matrix data, and unlabeled data.

Python Data Essentials - Numpy

Powerful N-dimensional array objects.

Python is one of The Most Popular Languages for Data Science, and because of this adoption by the data science community, we have libraries like NumPy, Pandas and Matplotlib. NumPy at it’s core provides a powerful N-dimensional array objects in which we can perform linear algebra, Pandas give us data structures and data analysis tools, similar to working with a specialized database or powerful spreadsheets and finally Matplotlib to generate plots, histograms, power spectra, bar charts, error charts and scatterplots to name a few.

Reverse Proxy in Golang

Retrofit security proxy to prevent XSS and code injection.

Reverse proxies are standard components in many web architectures, from Nginx in front of php-fpm serving Drupal or Wordpress, to endless mixtures of load balancers, security appliances, and popular firewall applications. Reverse proxies differ from forward proxies in little but their intended implementation, be it service-side or client side. The following information is useful in either context. However, I focus on a service-side architecture. Further down this article, I’ll be going over the reasonably simple go code needed to develop a basic, yet production quality proxy, but first I’ll give you my take on why they solve so many problems and offer up my little workhorse, n2proxy.

Golang to Jupyter

Golang with Jupyter Notebooks

Jupyter Notbooks have been a popular technology in the Python data science community for a while now, especially in academics. Jupyter Notebooks are a way to mix inline, executable code with documentation in a presentation format. Best practices in organizing source code are not always the most efficient at communicating it’s functionality to a user. While the intention of a programming language is the abstraction of computational complexity into a simplified language humans can read and write, they must always weigh toward the efficiency of the primary interpreter, the computer.

Essential Python 3

Programming in Python

This article is a quick tour of basic Python 3 syntax, components and structure. I intend to balance a cheat sheet format with hello world style boilerplate. If you are already a software developer and need a quick refresh on Python then I hope you benefit from my notes below. I am a professional software developer for a software development company, and for that reason, I work with a lot of languages, expert in some, and ok in others.

CORS on Kubernetes Ingress Nginx

Painless CORS header configuration in Kubernetes

Using ingress-nginx on Kubernetes makes adding CORS headers painless. Kubernetes ingress-nginx uses annotations as a quick way to allow you to specify the automatic generation of an extensive list of common nginx configuration options. Example ingress configuration enabling CORS: apiVersion: extensions/v1beta1 kind: Ingress metadata: name: api namespace: fuse labels: app: api annotations: "true" "PUT, GET, POST, OPTIONS" "" "true" spec: rules: - host: http: paths: - backend: serviceName: api-example servicePort: 80 path: /api tls: - hosts: - api.

Basic Auth on Kubernetes Ingress

Basic Auth is supported by nearly every major web client, library, and utility.

Basic Auth is one of the oldest and easiest ways to secure a web page or API endpoint. Basic Auth does not have many features and lacks the sophistication of more modern access controls (see Ingress Nginx Auth Examples). However, Basic Auth is supported by nearly every major web client, library, and utility. Basic Auth is secure, stable and perfect for quick security on Kubernetes projects. Basic Auth can easily we swapped out later as requirements demand or provide a foundation for implementations such as OAuth 2 and JWT.

JWT Microservice, Tokenize Remote APIs

Using txToken to create JWT Tokens from JSON POST data.

txToken is a small high performance microservice utility container. txToken is used for adding JSON Web Token based security to existing or new API development. txToken is specifically for systems that communicate in JSON over HTTP. txToken is called from a client with a JSON post body and passes received JSON to a remote endpoint. JSON retrieved from a remote endpoint is used to create a JWT token with an HS256 symmetrically encrypted signature.

Let's Encrypt, Kubernetes

Automated, secure and free 443/https with signed x509 certificates for Ingress.

Use cert-manager to get port 443/https running with signed x509 certificates for Ingress on your Kubernetes Production Hobby Cluster. cert-manager is the successor to kube-lego and the preferred way to “automatically obtain browser-trusted certificates, without any human intervention.” using Let’s Encrypt. You need to install Helm first if you do not already have it. Otherwise, check out my article Helm on Custom Kubernetes, especially if you are following along with my Production Hobby Cluster guides.

Helm on Custom Kubernetes

Kubernetes package management.

Helm is the official package manager for Kubernetes. If you are looking to start using Helm or want to test its capabilities, I suggest you set up a Production Hobby Cluster. This article is a continuation of the Production Hobby Cluster configuration but should be entirely useful on its own. From - Helm has two parts: a client (helm) and a server (tiller) - Tiller runs inside of your Kubernetes cluster and manages releases (installations) of your charts.

Kubernetes Custom Upstream DNS

Customize upstream DNS resolution.

Customize the Upstream Nameservers used by kube-dns by Pods when looking up external hostnames from within a Kubernetes cluster. I found that adding custom Upstream Nameservers to my kube-dns solved many issues encountered in in the past with external hostname resolution on individual Pods. If you want to experiment on a production-like cluster, I suggest reading my article “Production Hobby Cluster” for a guide on setting up a fun, cheap-yet-robust experimental cluster.

Ingress on Custom Kubernetes

Setting up ingress-nginx on a custom cluster.

There are more than a handful of ways to set up port 80 and 443 web ingress on a custom Kubernetes cluster. Specifically a bare metal cluster. If you are looking to experiment or learn on a non-production cluster, but something more true to production than minikube, I suggest you check out my previous article Production Hobby Cluster, a step-by-step guide for setting up a custom production capable Kubernetes cluster.

kubectl Context Multiple Clusters

Managing multiple clusters with kubectl.

I use a few Kubernetes clusters on a daily basis, and I use kubectl to access and configure them from my workstation. There are dozens of ways to configure kubectl however I find the following method the easiest for me to manage and not make a mess. I also set up test clusters from time-to-time, and so keeping my configs organized is, so I don’t confuse myself or make a mess.